PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision
This addresses the challenge of automating CAD sketch parameterization for designers and engineers, particularly for hand-drawn sketches, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of converting 2D CAD sketch images into parametric primitives for CAD software, using a self-supervised framework that reduces the need for parameter-level annotations and achieves reasonable performance with minimal fine-tuning data.
This work introduces PICASSO, a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images. PICASSO converts a given CAD sketch image into parametric primitives that can be seamlessly integrated into CAD software. Our framework leverages rendering self-supervision to enable the pre-training of a CAD sketch parameterization network using sketch renderings only, thereby eliminating the need for corresponding CAD parameterization. Thus, we significantly reduce reliance on parameter-level annotations, which are often unavailable, particularly for hand-drawn sketches. The two primary components of PICASSO are (1) a Sketch Parameterization Network (SPN) that predicts a series of parametric primitives from CAD sketch images, and (2) a Sketch Rendering Network (SRN) that renders parametric CAD sketches in a differentiable manner and facilitates the computation of a rendering (image-level) loss for self-supervision. We demonstrate that the proposed PICASSO can achieve reasonable performance even when finetuned with only a small number of parametric CAD sketches. Extensive evaluation on the widely used SketchGraphs and CAD as Language datasets validates the effectiveness of the proposed approach on zero- and few-shot learning scenarios.